June 23, 2014

On May 30, the Federal Reserve Bank of Cleveland generously allowed me some time to speak at their conference on Inflation, Monetary Policy, and the Public. The purpose of my remarks was to describe the motivations and methods behind some of the alternative measures of the inflation experience that my coauthors and I have produced in support of monetary policy.

In this, and the following two blogs, I'll be posting a modestly edited version of that talk. A full version of my prepared remarks will be posted along with the third installment of these posts.

The ideas expressed in these blogs and the related speech are my own, and do not necessarily reflect the views of the Federal Reserve Banks of Atlanta or Cleveland.

Part 1: The median CPI and other trimmed-mean estimators

A useful place to begin this conversation, I think, is with the following chart, which shows the monthly change in the Consumer Price Index (CPI) (through April).

The monthly CPI often swings between a negative reading and a reading in excess of 5 percent. In fact, in only about one-third of the readings over the past 16 years was the monthly, annualized seasonally adjusted CPI within a percentage point of 2 percent, which is the FOMC's longer-term inflation target. (Officially, the FOMC's target is based on the Personal Consumption Expenditures price index, but these and related observations hold for that price index equally well.)

How should the central bank think about its price-stability mandate within the context of these large monthly CPI fluctuations? For example, does April's 3.2 percent CPI increase argue that the FOMC ought to do something to beat back the inflationary threat? I don't speak for the FOMC, but I doubt it. More likely, there were some unusual price movements within the CPI's market basket that can explain why the April CPI increase isn't likely to persist. But the presumption that one can distinguish the price movements we should pay attention to from those that we should ignore is a risky business.

The Economistretells a conversation with Stephen Roach, who in the 1970s worked for the Federal Reserve under Chairman Arthur Burns. Roach remembers that when oil prices surged around 1973, Burns asked Federal Reserve Board economists to strip those prices out of the CPI "to get a less distorted measure. When food prices then rose sharply, they stripped those out too—followed by used cars, children's toys, jewellery, housing and so on, until around half of the CPI basket was excluded because it was supposedly 'distorted'" by forces outside the control of the central bank. The story goes on to say that, at least in part because of these actions, the Fed failed to spot the breadth of the inflationary threat of the 1970s.

I have a similar story. I remember a morning in 1991 at a meeting of the Federal Reserve Bank of Cleveland's board of directors. I was welcomed to the lectern with, "Now it's time to see what Mike is going to throw out of the CPI this month." It was an uncomfortable moment for me that had a lasting influence. It was my motivation for constructing the Cleveland Fed's median CPI.

I am a reasonably skilled reader of a monthly CPI release. And since I approached each monthly report with a pretty clear idea of what the actual rate of inflation was, it was always pretty easy for me to look across the items in the CPI market basket and identify any offending—or "distorted"—price change. Stripping these items from the price statistic revealed the truth—and confirmed that I was right all along about the actual rate of inflation.

Let me show you what I mean by way of the April CPI report. The next chart shows the annualized percentage change for each component in the CPI for that month. These are shown on the horizontal axis. The vertical axis shows the weight given to each of these price changes in the computation of the overall CPI. Taken as a whole, the CPI jumped 3.2 percent in April. But out there on the far right tail of this distribution are gasoline prices. They rose about 32 percent for the month. If you subtract out gasoline from the April CPI report, you get an increase of 2.1 percent. That's reasonably close to price stability, so we can stop there—mission accomplished.

But here's the thing: there is no such thing as a "nondistorted" price. All prices are being influenced by market forces and, once influenced, are also influencing the prices of all the other goods in the market basket.

What else is out there on the tails of the CPI price-change distribution? Lots of stuff. About 17 percent of things people buy actually declined in price in April while prices for about 13 percent of the market basket increased at rates above 5 percent.

But it's not just the tails of this distribution that are worth thinking about. Near the center of this price-change distribution is a very high proportion of things people buy. For example, price changes within the fairly narrow range of between 1.5 percent and 2.5 percent accounted for about 26 percent of the overall CPI market basket in the April report.

The April CPI report is hardly unusual. The CPI report is commonly one where we see a very wide range of price changes, commingled with an unusually large share of price increases that are very near the center of the price-change distribution. Statisticians call this a distribution with a high level of "excess kurtosis."

The following chart shows what an average monthly CPI price report looks like. The point of this chart is to convince you that the unusual distribution of price changes we saw in the April CPI report is standard fare. A very high proportion of price changes within the CPI market basket tends to remain close to the center of the distribution, and those that don't tend to be spread over a very wide range, resulting in what appear to be very elongated tails.

And this characterization of price changes is not at all special to the CPI. It characterizes every major price aggregate I have ever examined, including the retail price data for Brazil, Argentina, Mexico, Columbia, South Africa, Israel, the United Kingdom, Sweden, Canada, New Zealand, Germany, Japan, and Australia.

Why do price change distributions have peaked centers and very elongated tails? At one time, Steve Cecchetti and I speculated that the cost of unplanned price changes—called menu costs—discourage all but the most significant price adjustments. These menu costs could create a distribution of observed price changes where a large number of planned price adjustments occupy the center of the distribution, commingled with extreme, unplanned price adjustments that stretch out along its tails.

But absent a clear economic rationale for this unusual distribution, it presents a measurement problem and an immediate remedy. The problem is that these long tails tend to cause the CPI (and other weighted averages of prices) to fluctuate pretty widely from month to month, but they are, in a statistical sense, tethered to that large proportion of price changes that lie in the center of the distribution.

So my belated response to the Cleveland board of directors was the computation of the weighted median CPI (which I first produced with Chris Pike). This statistic considers only the middle-most monthly price change in the CPI market basket, which becomes the representative aggregate price change. The median CPI is immune to the obvious analyst bias that I had been guilty of, while greatly reducing the volatility in the monthly CPI report in a way that I thought gave the Federal Reserve Bank of Cleveland a clearer reading of the central tendency of price changes.

How much one should trim from the tails isn't entirely obvious. We settled on the 16 percent trimmed mean for the CPI (that is, trimming the highest and lowest 8 percent from the tails of the CPI's price-change distribution) because this is the proportion that produced the smallest monthly volatility in the statistic while preserving the same trend as the all-items CPI.

The following chart shows the monthly pattern of the median CPI and the 16 percent trimmed-mean CPI relative to the all-items CPI. Both measures reduce the monthly volatility of the aggregate price measure by a lot—and even more so than by simply subtracting from the index the often-offending food and energy items.

But while the median CPI and the trimmed-mean estimators are often referred to as "core" inflation measures (and I am guilty of this myself), these measures are very different from the CPI excluding food and energy.

In fact, I would not characterize these trimmed-mean measures as "exclusionary" statistics at all. Unlike the CPI excluding food and energy, the median CPI and the assortment of trimmed-mean estimators do not fundamentally alter the underlying weighting structure of the CPI from month to month. As long as the CPI price change distribution is symmetrical, these estimators are designed to track along the same path as that laid out by the headline CPI. It's just that these measures are constructed so that they follow that path with much less volatility (the monthly variance in the median CPI is about 95 percent smaller than the all-items CPI and about 25 percent smaller than the CPI less food and energy).

I think of the trimmed-mean estimators and the median CPI as being more akin to seasonal adjustment than they are to the concept of core inflation. (Indeed, early on, Cecchetti and I showed that the median CPI and associated trimmed-mean estimates also did a good job of purging the data of its seasonal nature.) The median CPI and the trimmed-mean estimators are noise-reduced statistics where the underlying signal being identified is the CPI itself, not some alternative aggregation of the price data.

This is not true of the CPI excluding food and energy, nor necessarily of other so-called measures of "core" inflation. Core inflation measures alter the weights of the price statistic so that they can no longer pretend to be approximations of the cost of living. They are different constructs altogether.

The idea of "core" inflation is one of the topics of tomorrow's post.

By Mike Bryan, vice president and senior economist in the Atlanta Fed's research department

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August 30, 2013

Still Waiting for Takeoff...

On Thursday, we got a revised look at the economy’s growth rate in the second quarter. While the 2.5 percent annualized rate was a significant upward revision from the preliminary estimate, it comes off a mere 1.1 percent growth rate in the first quarter. That combines for a subpar first-half growth rate of 1.8 percent. OK, it’s growth, but not as strong as one would expect for a U.S. expansion and clearly a disappointment to the many forecasters who had once (again) expected this to be the year the U.S. economy shakes itself out of the doldrums.

What we can say about the report is that the revised second-quarter growth estimate is a decided improvement from the first quarter and a modest bump up from the recent four-quarter growth trend (1.6 percent). And there are some positive indicators within the GDP components. For example, real exports posted a strong turnaround last quarter, presumably benefiting from Europe’s emerging from its recession. And the negative influence of government spending cuts, while still evident in the data, was much smaller than during the previous two quarters. Oh, and business investment spending improved between the first and second quarters.

All good, but these data simply give us a better fix on where we were in the second quarter, not necessarily a good signal of where we are headed. To that we turn to our “nowcast” estimate for the third quarter based on the incoming monthly data (the evolution of which is shown in the table below).

A "nowcasting" exercise generates quarterly GDP estimates in real time. The technical details of this exercise are described here, but the idea is fairly simple. We use incoming data on 100-plus economic series to forecast 12 components of GDP for the current quarter. We then aggregate those forecasts of GDP components to get a current-quarter estimate of overall GDP growth.

We caution that unlike others, our nowcast involves no interpretation whatsoever of these data. In what is purely a statistical exercise, we let the data do all the speaking for themselves.

Given the first data point of July—the July jobs report—the nowcast for the third quarter was pretty bleak (1.1 percent). Things improved a few days later with the release of strong international trade data for June, and stepped up further with the June wholesale trade report. But the remainder of the recent data point to a third-quarter growth rate that is very close to the lackluster performance of the first half.

The July data were pretty disappointing on this score. The durable-goods numbers released a few days ago were quite weak, causing our nowcast, and those of the others we follow, to revise down the third-quarter growth estimate.

“I expect the rebound we have seen in the housing sector to continue.”

Check. Our nowcast wasn’t affected much by the housing starts data, but the existing sales numbers produced a positive boost to the estimate. Our nowcast’s estimate of residential investment growth in the third quarter is well under what we saw in the second quarter. But at 5.3 percent, the rebound looks to be continuing.

“I expect the recent improvement in exports to last.”

Unfortunately, the July trade numbers don’t get reported until next week. So we’re going to mark this one as missing in action. But as we said earlier, that June trade number was strong enough to cause our third-quarter nowcast to be revised up a bit.

“And I expect to see an easing of the public-sector spending drag at the federal, state, and local levels.”

Again, check. The July Treasury data indicated growth in government spending overall.

So the July data are a mixed bag: some positives, some disappointments, and some missing-in-actions. But if President Lockhart were to ask us (and something tells us he just might), we’re likely to say that on the basis of the July indicators, the “pickup in real GDP growth over the balance of 2013” isn’t yet very evident in the data.

This news isn’t likely to come as a big surprise to him. Again, here’s what he said publicly two weeks ago:

When I weigh the balance of risks around the medium-term outlook I laid out, I have some concerns about the potential for ambiguous or disappointing data. I also think that it is important to be realistic about the degree to which we are likely to have clarity in the near term about the direction of the economy. Both the quantity of information and the strength of the signal conveyed by the data will likely be limited. As of September, the FOMC will have in hand one more employment report, two reports on inflation, a revision to the second-quarter GDP data, and preliminary incoming signals about growth in the third quarter. I don't expect to have enough data to be sure of my outlook.

It’s still a little early to say with any confidence we won’t eventually see a pickup this quarter, and we can hope that the incoming August numbers show a more marked improvement. All we can say at this point is that after seeing most of the July data, it still feels like we’re stuck on the tarmac.

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August 16, 2013

GDP, Jobs, and Growth Accounting

U.S. worker productivity accelerated to a still-modest 0.9% annual pace between April and June after dropping the previous quarter.

The second-quarter gain...reversed a decline in the January-March quarter, when the Labor Department's revised numbers show productivity shrank at a 1.7% annual pace.

Labor costs rose at a 1.4% annual pace from April through June, reversing a revised 4.2% drop the previous quarter.

Productivity measures output per hour of work. Weak productivity suggests that companies may have to hire because they can't squeeze more work from their existing employees....

Productivity growth has been weaker recently, rising 1.5% in 2012 and 0.5% in 2011.

Annual productivity growth averaged 3.2% in 2009 and 3.3% in 2010. In records dating back to 1947, it's been about 2%.

Though not quite in the category of spectacular—and coming off revisions that if anything made things look weaker than previously thought—last quarter's uptick is a welcome development. Earlier this week, in a speech to the Atlanta Kiwanis club, Atlanta Fed President Dennis Lockhart laid out several scenarios with materially different implications for how the GDP and employment picture might play out over the next several years:

[I] believe that the recent low growth of productivity is probably just a temporary downdraft after the rather strong productivity growth when the economy emerged from recession.

If productivity growth rebounds to more typical levels, the coincidence of job gains at a pace of around 190,000 per month in recent months and GDP growth below 2 percent cannot persist. Again, it's a matter of arithmetic. Either GDP growth will rise to levels consistent with recent employment growth, or employment growth will fall to levels more consistent with the weak GDP data we've been witnessing.

I've got a working assumption on this question, and it is captured in the Atlanta Fed's baseline forecast for the second half of this year and 2014. This outlook calls for a pickup in real GDP growth over the balance of 2013, with a further step-up in economic activity as we move into 2014.

You can get a sense of this outlook by considering the output of one particular model that we use here at the Atlanta Fed. The model, which is purely statistical, gives us a view into how productivity, GDP, employment, and the unemployment rate might move together (along with other labor market variables like labor force participation and average hours worked). Here is the bottom line of an exercise that assumes GDP growth through 2015 comes in at about the central tendency of the projections from the Federal Reserve's June 2013 Summary of Economic Projections.

For this exercise, we have adjusted the 2013 growth forecast down slightly due to the weaker-than-expected growth in the first half of the year. Additionally, we have plugged in assumptions for productivity growth—1.5 percent per quarter (SAAR), the average gain over the past eight years—and nonfarm business output growth. We then let the model forecast the remaining variables, all of which are for the labor market:

The model forecasts employment gains in the neighborhood of what the economy has been generating over the past several years, and a steadily declining unemployment rate.

Now consider two "stall" scenarios in which GDP growth fails to get beyond 2.3 percent. The first of these scenarios is the one noted in the Lockhart Kiwanis speech, with productivity recovering but job growth falling off the pace:

From a policy perspective, this one may not cause too much handwringing about the appropriate course of action. The weak GDP growth is accompanied by a failure to make the type of progress on the unemployment rate that the FOMC has clearly articulated as the necessary condition for adjustments in policy rates:

[T]he Committee decided to keep the target range for the federal funds rate at 0 to 1/4 percent and currently anticipates that this exceptionally low range for the federal funds rate will be appropriate at least as long as the unemployment rate remains above 6-1/2 percent, inflation between one and two years ahead is projected to be no more than a half percentage point above the Committee's 2 percent longer-run goal, and longer-term inflation expectations continue to be well anchored.

Absent unforeseen issues with inflation, staying the course would seem to be in order.

But there is a second stall scenario in which productivity and GDP growth remain tepid, even as labor market indicators improve:

The difference in this experiment is that the expectations of those that President Lockhart referred to in his speech as the "innovation pessimists" are correct. Recent weakness in productivity growth reflects a fall in trend productivity growth. In this case, essentially identical labor market outcomes would nonetheless correspond to an economy that can't seem to hit "escape" velocity.

If it is clear that this configuration of outcomes is associated with a structural break in productivity growth, an argument against monetary policy stimulus would have some weight. After all, in most cases we don't expect the tools of monetary policy to fix structural efficiency problems.

But, alas, such clarity rarely arrives in real time. The experiments above give some sense of how difficult it can be to discover the right branch to follow on the policy decision tree.

By Dave Altig, executive vice president and research director of the Atlanta Fed

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The fundamental challenge with income statistics is that we are being confused by the terrible impact of a completely left tailed incomes distribution prior to the crisis with a job growth that is happening at the bottom of the pyramid, which effectively means that even though the job numbers look good, its impact on the GDP growth would be minuscule.

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August 02, 2013

What a Difference a Month Makes? Maybe Not Much

By most accounts, the July employment report released this morning was something of a disappointment, perhaps more because it fell short of expectations than for any absolute signal it sends about the state of the economy. To be sure, the 162,000 net jobs created in July were below June’s 12-month average, which itself ticked down a bit as a result of negative revisions to the May and June statistics.

“Ticked down a bit” is the operative phrase, as the average monthly jobs gain from May 2012 through June 2013 now registers at 189,000 as opposed to the 191,000 reported last month. With this month’s new data, the 12-month average gains (from June 2012 to July 2013) clock in at 190,000 jobs per month, still right on the trend that has prevailed over the past couple of years. In other words, not much has changed in the longer view of things.

Following last month’s employment report I offered up calculations from the Atlanta Fed’s Jobs Calculator™ regarding the dates at which these unemployment thresholds might be reached, under the assumptions that jobs gains average 191,000 per month going forward, the participation rate remains constant at the reported June level, and there will be no change in the relationship between employment statistics from the payroll (or establishment) survey (whence comes the headline jobs number) and the employment statistics from the household survey (statistics used to calculate the unemployment rate). All of these figures change month to month, so it may be useful to update that exercise with current statistics (with last month’s calculations noted parenthetically):

Not much change there. In fact, the unemployment rates in these calculations fall a little faster than last month’s calculations suggested, in part due to the ancillary assumptions on participation rates and the payroll-employment /household-employment ratio.

In the spirit of pessimism—an economist’s university-given right—I’ll ask: what if the latest 162,000-job-gain number is closer than the trailing 12-month average to what we will experience going forward? Easiest enough to explore:

I will leave it to you to decide whether the differences imply important policy distinctions.

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June 07, 2013

The Hiring Forecasts of Small Firms: Will the Pace of Employment Growth Pick Up?

The U.S. Bureau of Labor Statistics (BLS) announced today that the U.S. labor market added 175,000 payroll jobs in May, continuing a trend of steady but disappointingly slow employment growth. The employment recovery has been even slower among small firms. Will it pick up in the coming 12 months? Results from the Atlanta Fed's latest survey of small businesses in the Southeast suggest that employment growth among small firms will continue but not necessarily at a faster pace.

Since the recession began, changes in employment have been asymmetric across firm size. In contrast to large firms, employment at small and medium sized businesses began decreasing earlier, declined more, and, by last March, was a little further from its prerecession level. As of the first quarter of 2012, employment at firms with fewer than 500 employees was 5 percent below prerecession levels, compared to just 2 percent for firms with more than 500 employees. So why is employment at small firms not recovering as quickly as employment at large firms? Is it poised to accelerate and perhaps catch up?

While the Business Employment Dynamics data series from the BLS only go through first-quarter 2012 (chart 1), we can use our semi-annual survey of small business in the Southeast to find out a little more about the experiences of small firms through first-quarter 2013 as well look at their forecasts through the first quarter of 2014. Four-hundred-seventy-eight firms across the industry and age spectrum participated in the first-quarter 2013 survey, which was conducted during the first three weeks in April. Although the survey is not a random sample, the results are weighted to make them more representative of a national distribution.

When asked about changes in employment over the period Q1 2012 to Q1 2013, employer firms on net said there was almost no change. Slightly more than 40 percent of firms said they had not altered employment levels. The remainder of the responses were distributed pretty evenly between "expansion" and "contraction". As you can see in chart 2, the distribution of firms creating jobs was almost a mirror image of the distribution of firms shedding jobs in terms of the magnitude of change.

In addition to asking about changes during the past 12 months, the survey probed small firms about their expectations for the coming 12 months. Using the power of our panel data set, we can compare the expectations of firms that took the survey exactly one year ago with their actual hiring activity during that time period to determine how accurately firms predict what the future holds and whether these hiring plans are indeed good forecasts of future activity.

As it turns out, the 184 firms participating in both surveys came pretty close to meeting their hiring expectations. However, they did tend to overestimate the extent to which employment would increase (or underestimate the extent to which it would decrease), regardless of how well firms were performing at the time they made their forecast (see chart 3). For example, firms that had recently experienced reductions in their workforce expected the greatest positive change in the pace of hiring, and in fact went on to report the highest actual change during this period. Firms that had not changed their employment levels recently or had changed them by up to 10 percent expected very little growth—on average, they achieved just slightly less than expected. Regardless of how well the firm had recently performed (in terms of employment growth in the previous period), the degree to which hiring increased or downsizing decreased was less pronounced than anticipated.

Small firms are reasonably good at predicting the direction and relative magnitude of their employment growth, but on average tend to overestimate. For this reason, it might be useful to examine changes in the hiring expectations index (as opposed to changes in the pace of employment growth) when trying to understand how the forecast of firms participating in the survey might translate into actual employment growth of small firms in the Southeast.

Chart 4 shows the hiring index of firms across four broad industry groups. In the first quarter of 2013, the index for hiring in the coming 12 months was essentially unchanged from the Q3 2012 survey, and significantly below that of the Q1 2012 survey. The only industry whose employment forecast was notably positive was the construction and real estate industry. Firms in that category have been steadily increasing their hiring forecasts since the third quarter of 2011.

The fact that hiring expectations did not improve in the first-quarter survey leads to another, perhaps more important question: Why didn't they?

One contributing factor that could be having a particularly large impact on hiring expectations is rocky sales. Firms may be less willing to hire if they are uncertain about the future or if they do not expect consistent sales growth. Indeed, by looking at the experiences of firms in the past 12 months, we can tell that there is a clear correlation between rising sales and rising employment. As chart 5 shows, half of employer firms reported a recent rise in sales, and the more sales had risen, the more likely firms were to have increased their workforce.

A couple of questions that arise from chart 5 are: What about the firms that recently experienced sales growth but didn't hire? Are they planning to hire in the coming 12 months? About one-third of firms say "yes". One driving factor in that decision appears to be sustained sales growth; another is reduced uncertainty. As chart 6 makes apparent, the sales expectations of firms in this group is higher on average for the one-third of firms that say they do plan to hire in the coming 12 months than for the two-thirds who do not. All the firms in the hiring group also expect sales growth to continue, with the most common response being greater than 10 percent growth. In contrast, while 77 percent of firms in the not-hiring group anticipate sustained sales growth, the group’s most common response was lower than that of the hiring group: 1 percent to 5 percent.

Another factor that may be related to hiring is reduced uncertainty. Employer firms experiencing sales growth in the past 12 months are more likely to anticipate hiring if they perceive a decrease in uncertainty compared to six months ago. Seventy percent of firms that had a recent increase in sales and decreased uncertainty concerns relative to six months ago anticipate hiring in the coming year. In contrast, 46 percent of those who had experienced a recent increase in sales but also perceived heightened uncertainty anticipate hiring.

For now, the results suggest that uncertainty and rocky sales growth are negatively affecting the hiring plans of small firms and, unfortunately, that small firms are not likely to increase their rate of hiring in the next 12 months. However, if uncertainty eases and sales growth continues, small firms will likely revisit their hiring plans and the pace of hiring just might improve.

By Ellyn Terry, a senior economic analyst in the Atlanta Fed's research department

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June 01, 2012

Will labor force participation continue to rise?

The labor force participation rate ticked up in May, as did the rate of unemployment. As we have noted in the past, the near-term trajectory of the unemployment rate depends critically on what happens to the participation rate. So the question is, can we expect further upward changes in the participation rate? The answer depends a lot on the labor market attachment of those that are currently out of the labor force.

A few weeks ago, my frequent coauthor, Julie Hotchkiss, wrote about what we can gain from detailed labor market data about the activities of people who have exited the labor force. In her posting, she discussed the overall increase in exits from the labor force, with a focus on 25–54 year olds. Her work concluded that while people identified "Household Care" as the dominant activity for those not in the labor force, there has been a significant upward shift since the recession in those indicating "School" or "Other" as their primary reason for not being in the labor force. A supposition is that at least those that indicated they were in school would reenter the labor force at some point, doing so with a higher level of skills or, at least, with skills that are better aligned with labor demand. However, because we know little about those in the Other category, the future labor market attachment for them is less clear.

This post explores data on transitions into the labor force, primarily for those in the Other category. As in the earlier blog, the focus is on individuals aged 25–54, as retirement dominates the activity of older individuals not in the labor force and schooling dominates the activity of younger individuals not in the labor force.

One indicator of whether those in the Other group are planning to reenter the labor force is whether the individuals in this group are classified as marginally attached to the labor force. A nonparticipant who is marginally attached indicates they want employment or are available for employment. Also, they indicate having looked for a job in the previous year but not actively looking for a job at present. Using monthly data from the Current Population Survey (CPS) that are matched year over year, we see that the marginally attached workers do transition back into the labor force at twice the rate of all individuals who are not in the labor force, as chart 1 illustrates. These rates are relatively stable over time.

As chart 2 shows, a much higher proportion of individuals in the Other category are marginally attached to the labor force, compared to other types of nonparticipants. Moreover, the percentage of these marginally attached nonparticipants has increased from around 20 percent to 30 percent over the last three years.

This higher probability of marginally attached workers returning to the labor force combined with the significantly increased share of marginally attached workers in the Other category suggests that we should expect to find a higher share of those in the Other group returning to the labor force than we've seen in the past. But it turns out that this expected development is not what has happened. The Other group also includes individuals who are not marginally attached to the labor market, and their transition rates into the labor market have declined. On net, while the transition rate to employment is highest for the Other category (reflecting the large of share of marginally attached), the transition rate into the labor force does not fully reflect the increased level of marginal attachment to the labor force.

The group with the next highest transition rate to employment is in the School category, which reflects the inherent transitory nature of that activity. However, it is noteworthy that the school transition rate is lower than it was before the recession. This development reflects an increase in the share of individuals continuing to indicate that school is their primary reason for not participating in the labor force from one year to the next. And it suggests that the lower opportunity cost of attending school is influencing the decision to remain in school longer.

While these trends suggest that we could expect to see higher rates of return to the labor force going forward, this potential development will likely require a much better showing of jobs numbers than were seen today before kicking in.

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May 11, 2012

Labor force nonparticipants: So what are they doing?

As Dave Altig, Atlanta Fed research director, pointed out earlier this week in this blog post, there is a great deal of interest these days in the labor force participation rate—particularly its level and the direction it's going. The question that seems to be on everyone's mind is how many of the nonparticipants in the labor force can we expect to return to the market. The answer to this question has immediate implications for the unemployment rate (especially if all these nonparticipants were to return to unemployment rolls), and longer-term implications for economic growth—our economy needs workers to fuel production.

The analyses that I can find to date are all primarily focused on a statistical detangling of demographic versus behavioral changes, structural versus cyclical changes, and employment trend versus employment gap debates. But all of this discussion begs the question that my colleague, Melinda Pitts, and I have been investigating: What are these labor force nonparticipants doing? Perhaps an answer to that question will help us get a better handle on which nonparticipants are likely to return to the labor force in the near future.

The Current Population Survey (CPS), administered by the U.S. Bureau of Labor Statistics (BLS), asks labor force nonparticipants about their reason for absence (details of the CPS questionnaire are available from the NBER). The reason given by nonparticipants that gets most of the attention is "discouraged over job prospects." In April 2012, these people accounted for only 1.1 percent of all nonparticipants (41 percent of the marginally attached—those who want a job, are available to work, and searched in the previous year). The vast majority of nonparticipants are absent because of retirement, disability, going to school, caring for household members, or other reasons.

Using the latest survey data we have available (November 2011), we find that most nonparticipants are retired (48 percent); the share who are in school, disabled, or taking care of household members are 18 percent, 16 percent, and 15 percent, respectively; and the share in the category termed "Other" comes in at about 2 percent.

For purposes of better understanding the decline in labor force participation, however, we look at the reasons for absence given by people who leave the labor force. Those who have left the labor force are arguably more likely to return (depending on the reason, of course) than those who have never been in the labor force. A feature of the CPS allows us to track certain individuals from one year to the next, so we are able to identify people who leave the labor force. Chart 1 illustrates how individuals who are not in the labor force—but who were employed or unemployed the previous year—are distributed across the reasons for nonparticipation. The raw data are not seasonally adjusted, of course, so we plot the numbers as a 12-month moving average—this approach does not affect the overall observed trends in the data. In addition, we restrict our analysis here to those between the ages of 25 and 54, since retirement overwhelmingly dominates the nonparticipation decisions of older workers, and schooling dominates the nonparticipation decisions of younger workers.

Chart 1 illustrates what the labor force participation rates have been telling us. For every reason given for absence, except perhaps "Retired," the number of people leaving the labor force has increased during or after the recession of 2008. The most dramatic increases are seen among those people giving "School" and "Other" as a reason. However, since we are in search of changes in reasons that might be out of the ordinary, especially any significant upward shifts in nonparticipants giving a particular reason during and after the recession, we also look at how these folks leaving the labor force are distributed across the different reasons. This information will tell us whether the number of people giving one particular reason increased disproportionately compared with the other reasons.

Chart 2 plots the shares of all of those leaving the labor force (ages 25–54) giving each reason for their absence. Since the beginning of the recession, there has been a significant shift toward the reasons of "School" and "Other" among nonparticipants who have left the labor force within the previous year. The share levels attained by the reasons of "School" and "Other" are historically unprecedented by the end of the data series. These shifts also appear to have come mostly from a decline in the share of people leaving the workforce to take care of household members (HHcare). This is evidenced through the dramatic drop in the share giving the "HHcare" reason at the same time.

It is difficult to interpret the implications of the rise in share of "Other" as a reason for nonparticipation among those leaving the labor force, although this category may be capturing some of the discouraged workers. The implication for the rise in "School" is unmistakable, however. With reasonable expectations, these individuals should re-enter the labor force with enhanced—or at least better-aligned—skills that will be able to make a positive contribution to overall economic growth.

By Julie Hotchkiss, research economist and policy adviser in the Atlanta Fed's research department

Comments

What % of the 100k uptick in school attendees are military personnel with GI bill funding? This is a non-trivial surge in the % of people that have been previously employed, and who are now going back to school. Correcting for this trend would remove the noise of politically-based decisions from the signal of economically-based decisions, and so give us more insight into long term expectations.

The unemployment problem and the labor force deterioration problem have to be considered aspects of the same phenomenon unless there is a good reason not to.

People are going back to school to improve their job skills in hopes there will be work for them when they return. This is advocated by many. They will return to Starbucks with heavy debt loads.

We've seen computer skills over-learned, then financial sector training left millions in the lurch with high debts. The problem with the job market is the private sector is not producing jobs in the U.S. and the public sector is paralyzed.

The Fed's idea that it will lower rates and improve investment metrics or increase the wealth effect is convoluted and certainly is not working.

You really need to integrate these flow values. When you look at the employment-population ratio, that is a level number, while these are flows. If you looked at the number of working age people who were out of the labor force for different reasons, and chart that vs time, you will get a picture of the size of the crisis and why XX million people are not participating because of YY reason.

The numbers listed in the body of the text don't seem to match the color coded charts. What is the response rate of the survey? Also, what about illegal aliens leaving the country and people working under the table? Those numbers have to be substantial.

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May 10, 2012

A take on labor force participation and the unemployment rate

The March to April decline in the unemployment rate from 8.2 percent to 8.1 percent was arithmetically driven by yet another decline in the labor force participation rate (LFPR).

The decline in the LFPR, now at its lowest level since the early 1980s, is itself being influenced by a confounding mix of demographic change and other behavioral changes that nobody seems to understand—a point emphasized by a gaggle of blogs and bloggers such as Brad DeLong, Carpe Diem, Conversable Economist, Free Exchange, and Rortybomb, to name a few.

With respect to the first observation, in a previous post my colleague Julie Hotchkiss described how to use our Jobs Calculator to get a ballpark sense of what the unemployment rate would have been had the LFPR not changed. If you follow those procedures and assume that the LFPR had stayed at the March level of 63.8 percent instead of falling to 63.6 percent, the unemployment rate would have risen to 8.4 percent instead of falling to 8.1 percent.

It is clear that interpreting this sort of counterfactual experiment depends critically on how you think about the decline in the LFPR. The aforementioned post at Rortybomb cites two Federal Reserve studies—from the Chicago Fed and the Kansas City Fed—that attempt to disentangle the change in the LFPR that can be explained by trends in the age and composition of the labor force. These changes are presumably permanent and have little to do with questions of whether the labor market is performing up to snuff.

The following chart, which throws our own estimates into the mix, illustrates the evolution of the actual LFPR along with an estimate of the LFPR adjusted for demographic changes:

As the header on the chart indicates, our estimates suggest that roughly 40 percent of the change in the LFPR since 2000 can be accounted for by changes in age and composition of the population—in essentially the same range as the Chicago and Kansas City Fed studies. (If you are interested in the technical details you can find a description of the methodology used to generate the chart above, based on work by the University of Chicago's Rob Shimer.

In other words, 0.9 percentage points of the decline in the LFPR since the beginning of the past recession can be explained by demographic trends (as the baby boomers age, the labor force will grow more slowly than the total population [ages 16 and up]). Subtracting the demographic trends still leaves 1.5 percentage points to be explained, a number right in line with Brad DeLong's back-of-the-envelope calculation of "cyclical" LFPR change.

As DeLong's comments make clear, the interpretation of the nondemographic piece of the LFPR change requires, well, interpretation. And the consequences of connecting the dots between changes in the unemployment rate and broader labor market performance are enormous.

In the recently released Summary of Economic Projections following the last meeting of the Federal Reserve's Federal Open Market Committee, the midpoint of the projections for the unemployment rate at the end of 2013 is 7.5 percent. Turning again to our Jobs Calculator, we can get a sense of what sort of job creation over the next 20 months will be required given different values of the LFPR. For these estimates, I consider three alternatives: The LFPR stays at its April level, the LFPR reverts to our current estimate of the demographically adjusted level (that is, increases by 1.5 percentage points), and an intermediate case in which the LFPR increases by 0.7 percentage points—the lower end of DeLong's estimate of "people who really ought to be in the labor force right now, but who are not."

"Are [people who really ought to be in the labor force right now, but who are not] now part of the 'structurally' non-employed who we will never see back at work, barring a high-pressure economy of a kind we see at most once in a generation?"

As you can see, the answer to that question matters a lot to how we should think about progress on the unemployment rate going forward.

By Dave Altig, executive vice president and research director at the Atlanta Fed

Comments

Seeing as there was an event, the Great Financial Crisis, and employment and participation have both trended in the down direction, perhaps we should look at them as aspects of the same thing, a decaying job market. Thus, the jobs calculator is a good thing -- the unemployment rate adjusted to a steady participation rate is a very good metric for gauging the real state of the labor market.

To say that part of the change in participation is due to demographics certainly cannot be proven by the relatively primitive analysis cited from the University of Chicago. In previous times of stagnating wages, for example, participation went up, thinking of the late 1970s into the late 1980s.

The layman's take is that participation is going down because jobs cannot be had and people are making other arrangements, whether taking disability, early retirement, borrowing to go to school, or adapting in another manner. Certainly it is a common perception that if the economy picks up, there will be more people entering the labor force.

I posted this on Mark Thoma's "Economist's View" in response. I thought I'd repeat it here, too.
_________
Something is wrong here.

I perused the linked reports from the Chicago and Kansas City Fed's. The data in both reports show an INCREASE in Labor Force Participation Rate (LFPR) for workers over 55 between 2001 and 2011. Look at Chart 8 in the Kansas City report and Table 4 in the Chicago report. The LFBR increases for older workers.

The Kansas City report even offers a reason for the increase:

"The rise can be explained by longer term developments, such as improving health and longevity, the need to build retirement savings due to the shift away from defined-benefit pensions, and decreased availability of retiree health benefits (Kwok and others)."

But then BOTH reports go on to say that overall LFPR is declining due to older workers LEAVING the workforce. This is directly at odds with the data.

A LARGER percentage of of older workers are putting off retirement, and this proves that more older workers are retiring earlier??

I'm no economist (obviously), but as a layperson, I don't think I'd buy this product.

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March 09, 2012

What if...? Looking beyond this month's jobs numbers

Today's employment numbers for February illustrate that while mathematically simple, the relationship between employment, unemployment, and the labor force participation rate is complicated.

One might expect that we would have seen a drop in the unemployment rate in February, given the addition of an estimated 227,000 payroll jobs for the month (see the U.S. Bureau of Labor Statistics' Employment Situation for February 2012). However, the share of the working-age population in the labor force (or, rather, the labor force participation rate, LFPR) is estimated to have increased from 63.7 percent in January to 63.9 percent in February. A 0.2 percentage point increase in the LFPR is not unprecedented, but after a year of flat and declining labor force participation, it's notable. There are a lot of reasons why the supply of labor, as represented by the LFPR, rises and falls over time. In the short run, a decision of someone to enter (or re-enter) the labor force could be driven by a reassessment of job prospects. This sort of situation is why the LFPR might rise as an economy improves from a very weak position.

While not its primary purpose, the Federal Reserve Bank of Atlanta's Jobs Calculator, which was introduced last week, can help figure out roughly what the unemployment rate would have been if the LFPR had remained at its January level of 63.7 percent.

From the Jobs Calculator web page, first set the number of months to one. Then set the labor force participation rate to 63.7 percent. Next, adjust the unemployment rate until the average monthly change in payroll employment gets close to 227,000. (For example, an unemployment rate of 7.9 percent results in an estimated change in employment of 250,743, using data from the U.S. Bureau of Labor Statistics's Current Employment Survey. This calculation necessarily assumes that people enter and leave the labor force from unemployment and is only approximate because it's using February data.)

So, if the LFPR had remained at the 63.7 percent it was in January, the unemployment rate would have been roughly 8 percent in February.

Look for enhancements to the Jobs Calculator in the coming months that will make this sort of calculation more straightforward.

By Julie Hotchkiss, a research economist and policy adviser in the Atlanta Fed's research department

Comments

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March 02, 2012

How many jobs does it take? Introducing the Atlanta Fed's Jobs Calculator

When I began my career at the Atlanta Fed in 2003, the U.S. labor market had not yet started creating jobs on net again after the 2001 recession. The question being asked over and over was, "How many jobs does the U.S. economy need to create in order to lower the unemployment rate by a certain amount?" I even participated in the discussion by writing an Economic Reviewarticle on the subject.

Of course, the Federal Reserve's interest in how many jobs it takes to lower the unemployment rate comes directly from Section 2A of the Federal Reserve Act, which states:

"The Board of Governors of the Federal Reserve System and the Federal Open Market Committee shall maintain long run growth of the monetary and credit aggregates commensurate with the economy's long run potential to increase production, so as to promote effectively the goals of maximum employment, stable prices, and moderate long-term interest rates."

This passage is often referred to as the Fed's "dual mandate" for monetary policy. Put simply, the Fed wants to achieve (1) stable prices and (2) maximum employment. Reduction in the unemployment rate is commonly used as a measure of the progress toward the goal of maximum employment.

In a technical sense, answering the question "How many additional jobs over the next Y months are needed to lower the unemployment rate by X percentage points?" does not require a difficult calculation. But it does require some knowledge about the U.S. Bureau of Labor Statistics's (BLS) Household Survey, which gives us the official measure of the U.S. unemployment rate. This survey is based on estimates of the size of the labor force and the number of people employed that are inferred from a survey of individual households. The Household Survey differs from the BLS's Payroll Survey, which provides another estimate of employment from a survey of the payroll of individual businesses. Early each month, the estimate of employment from the Payroll Survey shares the spotlight with the Household Survey's estimate of the unemployment rate when the BLS releases its monthly employment report.

To calculate the change in employment needed to achieve a particular unemployment rate requires an assumption about how much the labor force will grow or an assumption about labor force participation given a particular population growth rate. The more the labor force grows (or the participation rate increases), the more jobs the economy needs to create, on net, to absorb the larger labor force.

In recent months, economists again (here and here) are asking (or pontificating on), "How many jobs does it take...?" To help answer that question, we at the Atlanta Fed have developed a new tool that will make the calculation for you. The tool—called the Jobs Calculator—is available on the Atlanta Fed's Center for Human Capital Studies' web page. (Readers should note that the calculator currently uses data from the January employment report, the most recent one available. When the February report is released on March 9, the data the calculator uses will be updated.)

Using the tool is as simple as choosing the target unemployment rate you want to achieve and when you want to achieve it. The Jobs Calculator produces the average number of jobs that need to be created, on net, per month in order to reach the target in the specified time period. You can even make some adjustments in the assumptions about labor force participation and population growth (and hence labor force growth). Of course, the calculator doesn't answer the questions of what numbers to plug in or why. That's up to you.

Please tell us what you think.

By Julie Hotchkiss, a research economist and policy adviser in the Atlanta Fed's research department

Comments

I like the tool! I understand that it's still fresh out of the early birth pangs so it will need some modifications and tinkering as time goes on.

Still, I found it very pleasing that more and more Fed employees are finding their voice and going online to blog.

As a layman, I've often found it easier to enjoy economics since the examples are those I am interested in. The learning process is uneven but at times surprisingly deep.

These tools may help those like me who want to make sense of the economic environment very quickly, but in our own fashion, and that's a good thing. Of course, calculating these things aren't hard but the time effort is the one greatly saved in and that is the big factor for me.

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